Mova · papers by stage

how it learns

How the parsers learn: perceptrons, instance-based learning, bootstrapping and the teacher loop. The 10 works of Mova's own list that this stage uses; measured marks a work whose effect Mova measured in its own experiments (7 here). Each title opens our card: the summary, how Mova uses the work, its references and who cites it. What the stage does is on Mova's pipeline page; every stage is on the main list.

• Instance-Based Learning Algorithmsmeasured
  David W. Aha, Dennis Kibler, Marc K. Albert · 1991

  What Mova took: prag/src/model.rs — the k-NN / IB1 classifier, one of three models behind a shared Classifier trait in the pragmatics crate (sentence form, speech act, indirectness, hedging, polarity, voice).

• IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithmsmeasured
  Walter Daelemans, Antal van den Bosch, Ton Weijters · 1997

  What Mova took: prag/src/model.rs — IGTree is one of three models behind a shared Classifier trait in the pragmatics crate, trained on the same features as the perceptron and k-NN.

• Assessing the Reliability of Statistical Software: Part Imeasured
  B. D. McCullough · 1998

  What Mova took: mlab/tests/ext_nist.rs — the MATLAB-compatible numeric engine mlab is tested on NIST StRD datasets (11 regressions, 11 ANOVA, 9 univariate; data files under mlab/tests/data/ext/nist/).

• Discriminative Reranking for Natural Language Parsingmeasured
  Michael Collins · 2000

  What Mova took: math/src/steps.rs ("top-K reranking") — the arithmetic word-problem solver's beam-search perceptron produces the top-K distinct final expression trees (Model::topk, candidates with tree, score, value and actions), and a…

• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithmsmeasured
  Michael Collins · 2002

  What Mova took: en/src/ptag.rs: the English PTB part-of-speech tagger is an averaged perceptron in the spirit of this paper, run as a left-to-right greedy/beam tagger (following Honnibal 2013) with hashed integer features and per-tag weights.

• Curriculum Learningmeasured
  Yoshua Bengio et al. · 2009

  What Mova took: math/src/bin/mathsolve.rs, flag --curriculum of the steps model (word-problem solver as a transition system trained by a structured perceptron, math/src/steps.rs): before normal training the model gets 3 epochs on problems…

• Structured Perceptron with Inexact Searchmeasured
  Liang Huang, Suphan Fayong, Yang Guo · 2012

  What Mova took: math/src/steps.rs (Model::run) — the arithmetic word-problem solver builds expression trees with a beam-search perceptron trained with early update by default.

• C4.5: Programs for Machine Learning
  J. Ross Quinlan · 1993

  What Mova took: prag/src/model.rs — the function gain_ratio computes (H(Y) − Σ p(v) H(Y|v)) / SplitInfo for each feature slot, following this book (and the same weighting as in the TiMBL memory-based learning package).

• Bagging Predictors
  Leo Breiman · 1996

  What Mova took: math/src/bin/mathsolve.rs (--bag) — for the arithmetic word-problem solver, two extra beam-search perceptron models are trained with reversed and with shuffled training order (instead of bootstrap resamples), and their top-K…

• Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks
  Claire Hobbs, R. Thomas McCoy · 2026

  What Mova took: en/seeds/errors/singular-subject-plural-verb.md: cited in the seed for the warning rules en.errors.singular-subject-vbp and en.errors.singular-subject-vbp-auxcop…